Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_autocorrelation.wasp
Title produced by software(Partial) Autocorrelation Function
Date of computationMon, 08 Dec 2008 11:50:07 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/08/t1228762252ksy1wwu31cdnion.htm/, Retrieved Thu, 16 May 2024 15:57:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=30691, Retrieved Thu, 16 May 2024 15:57:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact201
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [(Partial) Autocorrelation Function] [Unemployment - St...] [2008-12-08 17:24:07] [57850c80fd59ccfb28f882be994e814e]
F   P   [(Partial) Autocorrelation Function] [Unemployment - St...] [2008-12-08 18:00:18] [57850c80fd59ccfb28f882be994e814e]
F           [(Partial) Autocorrelation Function] [STEP 1 2] [2008-12-08 18:50:07] [e11d930c9e2984715c66c796cf63ef19] [Current]
Feedback Forum
2008-12-11 13:17:35 [72e979bcc364082694890d2eccc1a66f] [reply
De student geeft geen uitleg over wat er kan waargenomen worden in de Autocorrelation Function. Je ziet namelijk dat er een trend en seizoenaliteit aanwezig is. Om dit weg te werken moeten we dan d en D gelijk stellen aan 1 zodat we eenmaal trendmatig en eenmaal seizoenaal differentiëren. Dit is ook het resultaat dat bekomen werd met de Variance Reduction Matrix.
2008-12-13 19:51:53 [Stéphanie Claes] [reply
We nemen inderdaad seizonaliteit en een lange termijntrend weer. Dit stem overeen met het resultaat dat we bekwamen bij de variance reduction matrix, we gaan d en D moeten instellen op 1 om de trend en de seizonaliteit weg te werken. De lags werden correct ingesteld op 60, zo krijgen we een goed zicht op het geheel.
2008-12-13 20:43:41 [Li Tang Hu] [reply
omdat je de lambdawaarde verkeerd hebt ingevuld, heb je een andere grafiek. normaal zien we dat er een dalende lange termijn trend is en ook seizoenaliteit. deze laatste kunnen we zien adhv pieken op de lags 12, 24, 36,..
we zouden dus al kunnen vermoeden dat we zowel trendmatig als seizonaal gaan moeten differentieeren.
  2008-12-14 10:07:51 [Stéphanie Claes] [reply
Ik denk niet dat de lambdawaarde verkeerd werd ingevuld. Er werd hiervoor inderdaad geen berekening uitgevoerd, maar ik heb de berekening gemaakt (zie feedback bij step 1) en de beta was niet significant verschillend van 0, dus dan is de lambdatransformatie zinloos en nemen we als lambdawaarde gewoon 1, en deze staat hier ook op 1 (de student zal deze standaard laten staan hebben) en is in dit geval wel correct.
2008-12-14 10:41:36 [94a54c888ac7f7d6874c3108eb0e1808] [reply
Op de grafiek van de student nemen we inderdaad een lange termijn en seizonale trend weer. We halen deze er uit door d en D op 1 te stellen. Die waarde zijn berekend bij step 1.
2008-12-15 10:50:44 [Gilliam Schoorel] [reply
Hier kan je een lange termijn trend zien. Hier is er ook sprake van seizoenaliteit die je door te diff moet wegwerken.
Je hebt hier zelf echter geen conclusie gegeven bij deze stap.
2008-12-15 12:47:43 [Toon Wouters] [reply
Bij deze autocorrelatie grafiek kunnen we voor de eerste 8 coëfficiënten een significant dalend verloop zien en voor deze eerste coëfficiënten is ook een soort hangmatpatroon vast te stellen. Maar uit deze berekening kunnen we nog niet concluderen dat er een lange termijn trend aanwezig is en seizoenaliteit aanwezig is. Verder onderzoek via de spectrale analyse is nodig.

Post a new message
Dataseries X:
569323
579714
577992
565464
547344
554788
562325
560854
555332
543599
536662
542722
593530
610763
612613
611324
594167
595454
590865
589379
584428
573100
567456
569028
620735
628884
628232
612117
595404
597141
593408
590072
579799
574205
572775
572942
619567
625809
619916
587625
565742
557274
560576
548854
531673
525919
511038
498662
555362
564591
541657
527070
509846
514258
516922
507561
492622
490243
469357
477580
528379




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30691&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30691&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30691&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ 72.249.76.132







Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.8846856.90960
20.6973695.44660
30.5447384.25453.7e-05
40.4580683.57760.000343
50.4262463.32910.000741
60.3914423.05730.001657
70.3256582.54350.006763
80.2483951.940.028502
90.2302671.79840.038527
100.2793612.18190.01649
110.3697952.88820.002678
120.4096423.19940.001093
130.2865292.23790.014446
140.1136110.88730.189194
15-0.0265-0.2070.418361
16-0.106109-0.82870.205241
17-0.141203-1.10280.137216
18-0.176257-1.37660.086832
19-0.231046-1.80450.038043
20-0.292195-2.28210.012992
21-0.291825-2.27920.013083
22-0.233152-1.8210.036758
23-0.143093-1.11760.134062
24-0.091479-0.71450.238829
25-0.151613-1.18410.120477
26-0.251269-1.96250.027136
27-0.322125-2.51590.007261
28-0.344506-2.69070.004594
29-0.33856-2.64420.005197
30-0.334085-2.60930.005699
31-0.344258-2.68870.004618
32-0.35451-2.76880.003721
33-0.318831-2.49010.007755
34-0.245834-1.920.029767
35-0.150126-1.17250.122773
36-0.083624-0.65310.258065
37-0.100201-0.78260.218447
38-0.151415-1.18260.120781
39-0.178423-1.39350.084259
40-0.175135-1.36780.088189
41-0.157506-1.23020.11168
42-0.142581-1.11360.134912
43-0.139056-1.08610.140863
44-0.13578-1.06050.146555
45-0.102666-0.80180.212877
46-0.042584-0.33260.370291
470.0275350.21510.41522
480.0715880.55910.289064
490.0674810.5270.300037
500.0391830.3060.380312
510.0177870.13890.444986
520.0083250.0650.474185
530.0047310.0370.485322
540.0018550.01450.494244
55-0.00897-0.07010.472189
56-0.025764-0.20120.420597
57-0.033704-0.26320.396628
58-0.025321-0.19780.421944
59-0.010948-0.08550.466069
60-0.00204-0.01590.493671

\begin{tabular}{lllllllll}
\hline
Autocorrelation Function \tabularnewline
Time lag k & ACF(k) & T-STAT & P-value \tabularnewline
1 & 0.884685 & 6.9096 & 0 \tabularnewline
2 & 0.697369 & 5.4466 & 0 \tabularnewline
3 & 0.544738 & 4.2545 & 3.7e-05 \tabularnewline
4 & 0.458068 & 3.5776 & 0.000343 \tabularnewline
5 & 0.426246 & 3.3291 & 0.000741 \tabularnewline
6 & 0.391442 & 3.0573 & 0.001657 \tabularnewline
7 & 0.325658 & 2.5435 & 0.006763 \tabularnewline
8 & 0.248395 & 1.94 & 0.028502 \tabularnewline
9 & 0.230267 & 1.7984 & 0.038527 \tabularnewline
10 & 0.279361 & 2.1819 & 0.01649 \tabularnewline
11 & 0.369795 & 2.8882 & 0.002678 \tabularnewline
12 & 0.409642 & 3.1994 & 0.001093 \tabularnewline
13 & 0.286529 & 2.2379 & 0.014446 \tabularnewline
14 & 0.113611 & 0.8873 & 0.189194 \tabularnewline
15 & -0.0265 & -0.207 & 0.418361 \tabularnewline
16 & -0.106109 & -0.8287 & 0.205241 \tabularnewline
17 & -0.141203 & -1.1028 & 0.137216 \tabularnewline
18 & -0.176257 & -1.3766 & 0.086832 \tabularnewline
19 & -0.231046 & -1.8045 & 0.038043 \tabularnewline
20 & -0.292195 & -2.2821 & 0.012992 \tabularnewline
21 & -0.291825 & -2.2792 & 0.013083 \tabularnewline
22 & -0.233152 & -1.821 & 0.036758 \tabularnewline
23 & -0.143093 & -1.1176 & 0.134062 \tabularnewline
24 & -0.091479 & -0.7145 & 0.238829 \tabularnewline
25 & -0.151613 & -1.1841 & 0.120477 \tabularnewline
26 & -0.251269 & -1.9625 & 0.027136 \tabularnewline
27 & -0.322125 & -2.5159 & 0.007261 \tabularnewline
28 & -0.344506 & -2.6907 & 0.004594 \tabularnewline
29 & -0.33856 & -2.6442 & 0.005197 \tabularnewline
30 & -0.334085 & -2.6093 & 0.005699 \tabularnewline
31 & -0.344258 & -2.6887 & 0.004618 \tabularnewline
32 & -0.35451 & -2.7688 & 0.003721 \tabularnewline
33 & -0.318831 & -2.4901 & 0.007755 \tabularnewline
34 & -0.245834 & -1.92 & 0.029767 \tabularnewline
35 & -0.150126 & -1.1725 & 0.122773 \tabularnewline
36 & -0.083624 & -0.6531 & 0.258065 \tabularnewline
37 & -0.100201 & -0.7826 & 0.218447 \tabularnewline
38 & -0.151415 & -1.1826 & 0.120781 \tabularnewline
39 & -0.178423 & -1.3935 & 0.084259 \tabularnewline
40 & -0.175135 & -1.3678 & 0.088189 \tabularnewline
41 & -0.157506 & -1.2302 & 0.11168 \tabularnewline
42 & -0.142581 & -1.1136 & 0.134912 \tabularnewline
43 & -0.139056 & -1.0861 & 0.140863 \tabularnewline
44 & -0.13578 & -1.0605 & 0.146555 \tabularnewline
45 & -0.102666 & -0.8018 & 0.212877 \tabularnewline
46 & -0.042584 & -0.3326 & 0.370291 \tabularnewline
47 & 0.027535 & 0.2151 & 0.41522 \tabularnewline
48 & 0.071588 & 0.5591 & 0.289064 \tabularnewline
49 & 0.067481 & 0.527 & 0.300037 \tabularnewline
50 & 0.039183 & 0.306 & 0.380312 \tabularnewline
51 & 0.017787 & 0.1389 & 0.444986 \tabularnewline
52 & 0.008325 & 0.065 & 0.474185 \tabularnewline
53 & 0.004731 & 0.037 & 0.485322 \tabularnewline
54 & 0.001855 & 0.0145 & 0.494244 \tabularnewline
55 & -0.00897 & -0.0701 & 0.472189 \tabularnewline
56 & -0.025764 & -0.2012 & 0.420597 \tabularnewline
57 & -0.033704 & -0.2632 & 0.396628 \tabularnewline
58 & -0.025321 & -0.1978 & 0.421944 \tabularnewline
59 & -0.010948 & -0.0855 & 0.466069 \tabularnewline
60 & -0.00204 & -0.0159 & 0.493671 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30691&T=1

[TABLE]
[ROW][C]Autocorrelation Function[/C][/ROW]
[ROW][C]Time lag k[/C][C]ACF(k)[/C][C]T-STAT[/C][C]P-value[/C][/ROW]
[ROW][C]1[/C][C]0.884685[/C][C]6.9096[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.697369[/C][C]5.4466[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]0.544738[/C][C]4.2545[/C][C]3.7e-05[/C][/ROW]
[ROW][C]4[/C][C]0.458068[/C][C]3.5776[/C][C]0.000343[/C][/ROW]
[ROW][C]5[/C][C]0.426246[/C][C]3.3291[/C][C]0.000741[/C][/ROW]
[ROW][C]6[/C][C]0.391442[/C][C]3.0573[/C][C]0.001657[/C][/ROW]
[ROW][C]7[/C][C]0.325658[/C][C]2.5435[/C][C]0.006763[/C][/ROW]
[ROW][C]8[/C][C]0.248395[/C][C]1.94[/C][C]0.028502[/C][/ROW]
[ROW][C]9[/C][C]0.230267[/C][C]1.7984[/C][C]0.038527[/C][/ROW]
[ROW][C]10[/C][C]0.279361[/C][C]2.1819[/C][C]0.01649[/C][/ROW]
[ROW][C]11[/C][C]0.369795[/C][C]2.8882[/C][C]0.002678[/C][/ROW]
[ROW][C]12[/C][C]0.409642[/C][C]3.1994[/C][C]0.001093[/C][/ROW]
[ROW][C]13[/C][C]0.286529[/C][C]2.2379[/C][C]0.014446[/C][/ROW]
[ROW][C]14[/C][C]0.113611[/C][C]0.8873[/C][C]0.189194[/C][/ROW]
[ROW][C]15[/C][C]-0.0265[/C][C]-0.207[/C][C]0.418361[/C][/ROW]
[ROW][C]16[/C][C]-0.106109[/C][C]-0.8287[/C][C]0.205241[/C][/ROW]
[ROW][C]17[/C][C]-0.141203[/C][C]-1.1028[/C][C]0.137216[/C][/ROW]
[ROW][C]18[/C][C]-0.176257[/C][C]-1.3766[/C][C]0.086832[/C][/ROW]
[ROW][C]19[/C][C]-0.231046[/C][C]-1.8045[/C][C]0.038043[/C][/ROW]
[ROW][C]20[/C][C]-0.292195[/C][C]-2.2821[/C][C]0.012992[/C][/ROW]
[ROW][C]21[/C][C]-0.291825[/C][C]-2.2792[/C][C]0.013083[/C][/ROW]
[ROW][C]22[/C][C]-0.233152[/C][C]-1.821[/C][C]0.036758[/C][/ROW]
[ROW][C]23[/C][C]-0.143093[/C][C]-1.1176[/C][C]0.134062[/C][/ROW]
[ROW][C]24[/C][C]-0.091479[/C][C]-0.7145[/C][C]0.238829[/C][/ROW]
[ROW][C]25[/C][C]-0.151613[/C][C]-1.1841[/C][C]0.120477[/C][/ROW]
[ROW][C]26[/C][C]-0.251269[/C][C]-1.9625[/C][C]0.027136[/C][/ROW]
[ROW][C]27[/C][C]-0.322125[/C][C]-2.5159[/C][C]0.007261[/C][/ROW]
[ROW][C]28[/C][C]-0.344506[/C][C]-2.6907[/C][C]0.004594[/C][/ROW]
[ROW][C]29[/C][C]-0.33856[/C][C]-2.6442[/C][C]0.005197[/C][/ROW]
[ROW][C]30[/C][C]-0.334085[/C][C]-2.6093[/C][C]0.005699[/C][/ROW]
[ROW][C]31[/C][C]-0.344258[/C][C]-2.6887[/C][C]0.004618[/C][/ROW]
[ROW][C]32[/C][C]-0.35451[/C][C]-2.7688[/C][C]0.003721[/C][/ROW]
[ROW][C]33[/C][C]-0.318831[/C][C]-2.4901[/C][C]0.007755[/C][/ROW]
[ROW][C]34[/C][C]-0.245834[/C][C]-1.92[/C][C]0.029767[/C][/ROW]
[ROW][C]35[/C][C]-0.150126[/C][C]-1.1725[/C][C]0.122773[/C][/ROW]
[ROW][C]36[/C][C]-0.083624[/C][C]-0.6531[/C][C]0.258065[/C][/ROW]
[ROW][C]37[/C][C]-0.100201[/C][C]-0.7826[/C][C]0.218447[/C][/ROW]
[ROW][C]38[/C][C]-0.151415[/C][C]-1.1826[/C][C]0.120781[/C][/ROW]
[ROW][C]39[/C][C]-0.178423[/C][C]-1.3935[/C][C]0.084259[/C][/ROW]
[ROW][C]40[/C][C]-0.175135[/C][C]-1.3678[/C][C]0.088189[/C][/ROW]
[ROW][C]41[/C][C]-0.157506[/C][C]-1.2302[/C][C]0.11168[/C][/ROW]
[ROW][C]42[/C][C]-0.142581[/C][C]-1.1136[/C][C]0.134912[/C][/ROW]
[ROW][C]43[/C][C]-0.139056[/C][C]-1.0861[/C][C]0.140863[/C][/ROW]
[ROW][C]44[/C][C]-0.13578[/C][C]-1.0605[/C][C]0.146555[/C][/ROW]
[ROW][C]45[/C][C]-0.102666[/C][C]-0.8018[/C][C]0.212877[/C][/ROW]
[ROW][C]46[/C][C]-0.042584[/C][C]-0.3326[/C][C]0.370291[/C][/ROW]
[ROW][C]47[/C][C]0.027535[/C][C]0.2151[/C][C]0.41522[/C][/ROW]
[ROW][C]48[/C][C]0.071588[/C][C]0.5591[/C][C]0.289064[/C][/ROW]
[ROW][C]49[/C][C]0.067481[/C][C]0.527[/C][C]0.300037[/C][/ROW]
[ROW][C]50[/C][C]0.039183[/C][C]0.306[/C][C]0.380312[/C][/ROW]
[ROW][C]51[/C][C]0.017787[/C][C]0.1389[/C][C]0.444986[/C][/ROW]
[ROW][C]52[/C][C]0.008325[/C][C]0.065[/C][C]0.474185[/C][/ROW]
[ROW][C]53[/C][C]0.004731[/C][C]0.037[/C][C]0.485322[/C][/ROW]
[ROW][C]54[/C][C]0.001855[/C][C]0.0145[/C][C]0.494244[/C][/ROW]
[ROW][C]55[/C][C]-0.00897[/C][C]-0.0701[/C][C]0.472189[/C][/ROW]
[ROW][C]56[/C][C]-0.025764[/C][C]-0.2012[/C][C]0.420597[/C][/ROW]
[ROW][C]57[/C][C]-0.033704[/C][C]-0.2632[/C][C]0.396628[/C][/ROW]
[ROW][C]58[/C][C]-0.025321[/C][C]-0.1978[/C][C]0.421944[/C][/ROW]
[ROW][C]59[/C][C]-0.010948[/C][C]-0.0855[/C][C]0.466069[/C][/ROW]
[ROW][C]60[/C][C]-0.00204[/C][C]-0.0159[/C][C]0.493671[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30691&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30691&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.8846856.90960
20.6973695.44660
30.5447384.25453.7e-05
40.4580683.57760.000343
50.4262463.32910.000741
60.3914423.05730.001657
70.3256582.54350.006763
80.2483951.940.028502
90.2302671.79840.038527
100.2793612.18190.01649
110.3697952.88820.002678
120.4096423.19940.001093
130.2865292.23790.014446
140.1136110.88730.189194
15-0.0265-0.2070.418361
16-0.106109-0.82870.205241
17-0.141203-1.10280.137216
18-0.176257-1.37660.086832
19-0.231046-1.80450.038043
20-0.292195-2.28210.012992
21-0.291825-2.27920.013083
22-0.233152-1.8210.036758
23-0.143093-1.11760.134062
24-0.091479-0.71450.238829
25-0.151613-1.18410.120477
26-0.251269-1.96250.027136
27-0.322125-2.51590.007261
28-0.344506-2.69070.004594
29-0.33856-2.64420.005197
30-0.334085-2.60930.005699
31-0.344258-2.68870.004618
32-0.35451-2.76880.003721
33-0.318831-2.49010.007755
34-0.245834-1.920.029767
35-0.150126-1.17250.122773
36-0.083624-0.65310.258065
37-0.100201-0.78260.218447
38-0.151415-1.18260.120781
39-0.178423-1.39350.084259
40-0.175135-1.36780.088189
41-0.157506-1.23020.11168
42-0.142581-1.11360.134912
43-0.139056-1.08610.140863
44-0.13578-1.06050.146555
45-0.102666-0.80180.212877
46-0.042584-0.33260.370291
470.0275350.21510.41522
480.0715880.55910.289064
490.0674810.5270.300037
500.0391830.3060.380312
510.0177870.13890.444986
520.0083250.0650.474185
530.0047310.0370.485322
540.0018550.01450.494244
55-0.00897-0.07010.472189
56-0.025764-0.20120.420597
57-0.033704-0.26320.396628
58-0.025321-0.19780.421944
59-0.010948-0.08550.466069
60-0.00204-0.01590.493671







Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.8846856.90960
2-0.39248-3.06540.001618
30.1787561.39610.083868
40.1006510.78610.217422
50.0902790.70510.241715
6-0.114094-0.89110.188188
7-0.060124-0.46960.320164
80.0118570.09260.463261
90.269662.10610.019659
100.1021250.79760.214091
110.165921.29590.09995
12-0.215378-1.68220.048826
13-0.605686-4.73067e-06
140.1798561.40470.082588
15-0.081922-0.63980.262339
16-0.049596-0.38740.34992
17-0.067257-0.52530.300641
18-0.026434-0.20650.41856
190.081960.64010.262242
20-0.00874-0.06830.4729
210.0263510.20580.418813
22-0.072706-0.56790.286109
23-0.050704-0.3960.34674
240.0348650.27230.393155
250.0281740.220.413286
26-0.088529-0.69140.245958
270.0432870.33810.36823
28-0.056032-0.43760.331602
290.0127580.09960.460476
300.0126290.09860.460875
31-0.009029-0.07050.472004
320.0497690.38870.349424
33-0.117988-0.92150.180207
34-0.024629-0.19240.424051
350.0755710.59020.278608
36-0.025754-0.20110.420626
37-0.04702-0.36720.357357
38-0.017422-0.13610.446106
390.0358220.27980.390296
40-0.045089-0.35220.362968
410.0167670.1310.44812
42-0.016203-0.12660.449856
43-0.008306-0.06490.474245
44-0.038991-0.30450.380881
450.0134450.1050.458355
460.0255990.19990.421097
47-0.093775-0.73240.233362
48-0.006771-0.05290.479
490.1549331.21010.115462
50-0.060546-0.47290.318994
51-0.129608-1.01230.157704
520.02170.16950.432989
53-0.003419-0.02670.489392
540.0188090.14690.441847
55-0.082122-0.64140.261835
56-0.097256-0.75960.225212
57-0.095475-0.74570.22936
58-0.027055-0.21130.416677
590.0394040.30780.379659
600.0364630.28480.388386

\begin{tabular}{lllllllll}
\hline
Partial Autocorrelation Function \tabularnewline
Time lag k & PACF(k) & T-STAT & P-value \tabularnewline
1 & 0.884685 & 6.9096 & 0 \tabularnewline
2 & -0.39248 & -3.0654 & 0.001618 \tabularnewline
3 & 0.178756 & 1.3961 & 0.083868 \tabularnewline
4 & 0.100651 & 0.7861 & 0.217422 \tabularnewline
5 & 0.090279 & 0.7051 & 0.241715 \tabularnewline
6 & -0.114094 & -0.8911 & 0.188188 \tabularnewline
7 & -0.060124 & -0.4696 & 0.320164 \tabularnewline
8 & 0.011857 & 0.0926 & 0.463261 \tabularnewline
9 & 0.26966 & 2.1061 & 0.019659 \tabularnewline
10 & 0.102125 & 0.7976 & 0.214091 \tabularnewline
11 & 0.16592 & 1.2959 & 0.09995 \tabularnewline
12 & -0.215378 & -1.6822 & 0.048826 \tabularnewline
13 & -0.605686 & -4.7306 & 7e-06 \tabularnewline
14 & 0.179856 & 1.4047 & 0.082588 \tabularnewline
15 & -0.081922 & -0.6398 & 0.262339 \tabularnewline
16 & -0.049596 & -0.3874 & 0.34992 \tabularnewline
17 & -0.067257 & -0.5253 & 0.300641 \tabularnewline
18 & -0.026434 & -0.2065 & 0.41856 \tabularnewline
19 & 0.08196 & 0.6401 & 0.262242 \tabularnewline
20 & -0.00874 & -0.0683 & 0.4729 \tabularnewline
21 & 0.026351 & 0.2058 & 0.418813 \tabularnewline
22 & -0.072706 & -0.5679 & 0.286109 \tabularnewline
23 & -0.050704 & -0.396 & 0.34674 \tabularnewline
24 & 0.034865 & 0.2723 & 0.393155 \tabularnewline
25 & 0.028174 & 0.22 & 0.413286 \tabularnewline
26 & -0.088529 & -0.6914 & 0.245958 \tabularnewline
27 & 0.043287 & 0.3381 & 0.36823 \tabularnewline
28 & -0.056032 & -0.4376 & 0.331602 \tabularnewline
29 & 0.012758 & 0.0996 & 0.460476 \tabularnewline
30 & 0.012629 & 0.0986 & 0.460875 \tabularnewline
31 & -0.009029 & -0.0705 & 0.472004 \tabularnewline
32 & 0.049769 & 0.3887 & 0.349424 \tabularnewline
33 & -0.117988 & -0.9215 & 0.180207 \tabularnewline
34 & -0.024629 & -0.1924 & 0.424051 \tabularnewline
35 & 0.075571 & 0.5902 & 0.278608 \tabularnewline
36 & -0.025754 & -0.2011 & 0.420626 \tabularnewline
37 & -0.04702 & -0.3672 & 0.357357 \tabularnewline
38 & -0.017422 & -0.1361 & 0.446106 \tabularnewline
39 & 0.035822 & 0.2798 & 0.390296 \tabularnewline
40 & -0.045089 & -0.3522 & 0.362968 \tabularnewline
41 & 0.016767 & 0.131 & 0.44812 \tabularnewline
42 & -0.016203 & -0.1266 & 0.449856 \tabularnewline
43 & -0.008306 & -0.0649 & 0.474245 \tabularnewline
44 & -0.038991 & -0.3045 & 0.380881 \tabularnewline
45 & 0.013445 & 0.105 & 0.458355 \tabularnewline
46 & 0.025599 & 0.1999 & 0.421097 \tabularnewline
47 & -0.093775 & -0.7324 & 0.233362 \tabularnewline
48 & -0.006771 & -0.0529 & 0.479 \tabularnewline
49 & 0.154933 & 1.2101 & 0.115462 \tabularnewline
50 & -0.060546 & -0.4729 & 0.318994 \tabularnewline
51 & -0.129608 & -1.0123 & 0.157704 \tabularnewline
52 & 0.0217 & 0.1695 & 0.432989 \tabularnewline
53 & -0.003419 & -0.0267 & 0.489392 \tabularnewline
54 & 0.018809 & 0.1469 & 0.441847 \tabularnewline
55 & -0.082122 & -0.6414 & 0.261835 \tabularnewline
56 & -0.097256 & -0.7596 & 0.225212 \tabularnewline
57 & -0.095475 & -0.7457 & 0.22936 \tabularnewline
58 & -0.027055 & -0.2113 & 0.416677 \tabularnewline
59 & 0.039404 & 0.3078 & 0.379659 \tabularnewline
60 & 0.036463 & 0.2848 & 0.388386 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30691&T=2

[TABLE]
[ROW][C]Partial Autocorrelation Function[/C][/ROW]
[ROW][C]Time lag k[/C][C]PACF(k)[/C][C]T-STAT[/C][C]P-value[/C][/ROW]
[ROW][C]1[/C][C]0.884685[/C][C]6.9096[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]-0.39248[/C][C]-3.0654[/C][C]0.001618[/C][/ROW]
[ROW][C]3[/C][C]0.178756[/C][C]1.3961[/C][C]0.083868[/C][/ROW]
[ROW][C]4[/C][C]0.100651[/C][C]0.7861[/C][C]0.217422[/C][/ROW]
[ROW][C]5[/C][C]0.090279[/C][C]0.7051[/C][C]0.241715[/C][/ROW]
[ROW][C]6[/C][C]-0.114094[/C][C]-0.8911[/C][C]0.188188[/C][/ROW]
[ROW][C]7[/C][C]-0.060124[/C][C]-0.4696[/C][C]0.320164[/C][/ROW]
[ROW][C]8[/C][C]0.011857[/C][C]0.0926[/C][C]0.463261[/C][/ROW]
[ROW][C]9[/C][C]0.26966[/C][C]2.1061[/C][C]0.019659[/C][/ROW]
[ROW][C]10[/C][C]0.102125[/C][C]0.7976[/C][C]0.214091[/C][/ROW]
[ROW][C]11[/C][C]0.16592[/C][C]1.2959[/C][C]0.09995[/C][/ROW]
[ROW][C]12[/C][C]-0.215378[/C][C]-1.6822[/C][C]0.048826[/C][/ROW]
[ROW][C]13[/C][C]-0.605686[/C][C]-4.7306[/C][C]7e-06[/C][/ROW]
[ROW][C]14[/C][C]0.179856[/C][C]1.4047[/C][C]0.082588[/C][/ROW]
[ROW][C]15[/C][C]-0.081922[/C][C]-0.6398[/C][C]0.262339[/C][/ROW]
[ROW][C]16[/C][C]-0.049596[/C][C]-0.3874[/C][C]0.34992[/C][/ROW]
[ROW][C]17[/C][C]-0.067257[/C][C]-0.5253[/C][C]0.300641[/C][/ROW]
[ROW][C]18[/C][C]-0.026434[/C][C]-0.2065[/C][C]0.41856[/C][/ROW]
[ROW][C]19[/C][C]0.08196[/C][C]0.6401[/C][C]0.262242[/C][/ROW]
[ROW][C]20[/C][C]-0.00874[/C][C]-0.0683[/C][C]0.4729[/C][/ROW]
[ROW][C]21[/C][C]0.026351[/C][C]0.2058[/C][C]0.418813[/C][/ROW]
[ROW][C]22[/C][C]-0.072706[/C][C]-0.5679[/C][C]0.286109[/C][/ROW]
[ROW][C]23[/C][C]-0.050704[/C][C]-0.396[/C][C]0.34674[/C][/ROW]
[ROW][C]24[/C][C]0.034865[/C][C]0.2723[/C][C]0.393155[/C][/ROW]
[ROW][C]25[/C][C]0.028174[/C][C]0.22[/C][C]0.413286[/C][/ROW]
[ROW][C]26[/C][C]-0.088529[/C][C]-0.6914[/C][C]0.245958[/C][/ROW]
[ROW][C]27[/C][C]0.043287[/C][C]0.3381[/C][C]0.36823[/C][/ROW]
[ROW][C]28[/C][C]-0.056032[/C][C]-0.4376[/C][C]0.331602[/C][/ROW]
[ROW][C]29[/C][C]0.012758[/C][C]0.0996[/C][C]0.460476[/C][/ROW]
[ROW][C]30[/C][C]0.012629[/C][C]0.0986[/C][C]0.460875[/C][/ROW]
[ROW][C]31[/C][C]-0.009029[/C][C]-0.0705[/C][C]0.472004[/C][/ROW]
[ROW][C]32[/C][C]0.049769[/C][C]0.3887[/C][C]0.349424[/C][/ROW]
[ROW][C]33[/C][C]-0.117988[/C][C]-0.9215[/C][C]0.180207[/C][/ROW]
[ROW][C]34[/C][C]-0.024629[/C][C]-0.1924[/C][C]0.424051[/C][/ROW]
[ROW][C]35[/C][C]0.075571[/C][C]0.5902[/C][C]0.278608[/C][/ROW]
[ROW][C]36[/C][C]-0.025754[/C][C]-0.2011[/C][C]0.420626[/C][/ROW]
[ROW][C]37[/C][C]-0.04702[/C][C]-0.3672[/C][C]0.357357[/C][/ROW]
[ROW][C]38[/C][C]-0.017422[/C][C]-0.1361[/C][C]0.446106[/C][/ROW]
[ROW][C]39[/C][C]0.035822[/C][C]0.2798[/C][C]0.390296[/C][/ROW]
[ROW][C]40[/C][C]-0.045089[/C][C]-0.3522[/C][C]0.362968[/C][/ROW]
[ROW][C]41[/C][C]0.016767[/C][C]0.131[/C][C]0.44812[/C][/ROW]
[ROW][C]42[/C][C]-0.016203[/C][C]-0.1266[/C][C]0.449856[/C][/ROW]
[ROW][C]43[/C][C]-0.008306[/C][C]-0.0649[/C][C]0.474245[/C][/ROW]
[ROW][C]44[/C][C]-0.038991[/C][C]-0.3045[/C][C]0.380881[/C][/ROW]
[ROW][C]45[/C][C]0.013445[/C][C]0.105[/C][C]0.458355[/C][/ROW]
[ROW][C]46[/C][C]0.025599[/C][C]0.1999[/C][C]0.421097[/C][/ROW]
[ROW][C]47[/C][C]-0.093775[/C][C]-0.7324[/C][C]0.233362[/C][/ROW]
[ROW][C]48[/C][C]-0.006771[/C][C]-0.0529[/C][C]0.479[/C][/ROW]
[ROW][C]49[/C][C]0.154933[/C][C]1.2101[/C][C]0.115462[/C][/ROW]
[ROW][C]50[/C][C]-0.060546[/C][C]-0.4729[/C][C]0.318994[/C][/ROW]
[ROW][C]51[/C][C]-0.129608[/C][C]-1.0123[/C][C]0.157704[/C][/ROW]
[ROW][C]52[/C][C]0.0217[/C][C]0.1695[/C][C]0.432989[/C][/ROW]
[ROW][C]53[/C][C]-0.003419[/C][C]-0.0267[/C][C]0.489392[/C][/ROW]
[ROW][C]54[/C][C]0.018809[/C][C]0.1469[/C][C]0.441847[/C][/ROW]
[ROW][C]55[/C][C]-0.082122[/C][C]-0.6414[/C][C]0.261835[/C][/ROW]
[ROW][C]56[/C][C]-0.097256[/C][C]-0.7596[/C][C]0.225212[/C][/ROW]
[ROW][C]57[/C][C]-0.095475[/C][C]-0.7457[/C][C]0.22936[/C][/ROW]
[ROW][C]58[/C][C]-0.027055[/C][C]-0.2113[/C][C]0.416677[/C][/ROW]
[ROW][C]59[/C][C]0.039404[/C][C]0.3078[/C][C]0.379659[/C][/ROW]
[ROW][C]60[/C][C]0.036463[/C][C]0.2848[/C][C]0.388386[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30691&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30691&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.8846856.90960
2-0.39248-3.06540.001618
30.1787561.39610.083868
40.1006510.78610.217422
50.0902790.70510.241715
6-0.114094-0.89110.188188
7-0.060124-0.46960.320164
80.0118570.09260.463261
90.269662.10610.019659
100.1021250.79760.214091
110.165921.29590.09995
12-0.215378-1.68220.048826
13-0.605686-4.73067e-06
140.1798561.40470.082588
15-0.081922-0.63980.262339
16-0.049596-0.38740.34992
17-0.067257-0.52530.300641
18-0.026434-0.20650.41856
190.081960.64010.262242
20-0.00874-0.06830.4729
210.0263510.20580.418813
22-0.072706-0.56790.286109
23-0.050704-0.3960.34674
240.0348650.27230.393155
250.0281740.220.413286
26-0.088529-0.69140.245958
270.0432870.33810.36823
28-0.056032-0.43760.331602
290.0127580.09960.460476
300.0126290.09860.460875
31-0.009029-0.07050.472004
320.0497690.38870.349424
33-0.117988-0.92150.180207
34-0.024629-0.19240.424051
350.0755710.59020.278608
36-0.025754-0.20110.420626
37-0.04702-0.36720.357357
38-0.017422-0.13610.446106
390.0358220.27980.390296
40-0.045089-0.35220.362968
410.0167670.1310.44812
42-0.016203-0.12660.449856
43-0.008306-0.06490.474245
44-0.038991-0.30450.380881
450.0134450.1050.458355
460.0255990.19990.421097
47-0.093775-0.73240.233362
48-0.006771-0.05290.479
490.1549331.21010.115462
50-0.060546-0.47290.318994
51-0.129608-1.01230.157704
520.02170.16950.432989
53-0.003419-0.02670.489392
540.0188090.14690.441847
55-0.082122-0.64140.261835
56-0.097256-0.75960.225212
57-0.095475-0.74570.22936
58-0.027055-0.21130.416677
590.0394040.30780.379659
600.0364630.28480.388386



Parameters (Session):
par1 = 60 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ;
Parameters (R input):
par1 = 60 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
R code (references can be found in the software module):
if (par1 == 'Default') {
par1 = 10*log10(length(x))
} else {
par1 <- as.numeric(par1)
}
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
par5 <- as.numeric(par5)
if (par2 == 0) {
x <- log(x)
} else {
x <- (x ^ par2 - 1) / par2
}
if (par3 > 0) x <- diff(x,lag=1,difference=par3)
if (par4 > 0) x <- diff(x,lag=par5,difference=par4)
bitmap(file='pic1.png')
racf <- acf(x,par1,main='Autocorrelation',xlab='lags',ylab='ACF')
dev.off()
bitmap(file='pic2.png')
rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF')
dev.off()
(myacf <- c(racf$acf))
(mypacf <- c(rpacf$acf))
lengthx <- length(x)
sqrtn <- sqrt(lengthx)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Autocorrelation Function',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Time lag k',header=TRUE)
a<-table.element(a,hyperlink('basics.htm','ACF(k)','click here for more information about the Autocorrelation Function'),header=TRUE)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,'P-value',header=TRUE)
a<-table.row.end(a)
for (i in 2:(par1+1)) {
a<-table.row.start(a)
a<-table.element(a,i-1,header=TRUE)
a<-table.element(a,round(myacf[i],6))
mytstat <- myacf[i]*sqrtn
a<-table.element(a,round(mytstat,4))
a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Partial Autocorrelation Function',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Time lag k',header=TRUE)
a<-table.element(a,hyperlink('basics.htm','PACF(k)','click here for more information about the Partial Autocorrelation Function'),header=TRUE)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,'P-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:par1) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,round(mypacf[i],6))
mytstat <- mypacf[i]*sqrtn
a<-table.element(a,round(mytstat,4))
a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')